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Regularization in kernel learning

Mendelson, Shahar; Neeman, Joseph


Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

CollectionsANU Research Publications
Date published: 2010
Type: Journal article
Source: The Annals of Statistics
DOI: 10.1214/09-AOS728


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